Research Interest

My research interest focuses on three aspects. First, I am interested in studying business policy in online platforms, especially knowledge contribution platform and e-commerce platform. Identifying causality is critical for policy making. I am familiar with using econometric tools for causal inference with observational data. My first-year paper and all three essays in my dissertation have adopted some causal inference tools, such as difference-in-differences with matching, instrumental variable, and random experiment design—A/B testing. Second, the adoption of machine learning and deep learning enriches IS research. The second essay of my dissertation and the statistical paper apply several natural language processing approaches. The third essay uses an artificial intelligence tool to perform image prediction. I consider the adoption of AI in IS as a trend and with a bright future. I am interested in studying related topics. Third, I have a relatively solid background in applied statistics. I am fond of applying statistical tools under the IS context. I have one paper under review in Management Science and this paper makes refinement on conventional canonical correlation analysis and tries to solve dimension reduction problem under social media context, which contains high-dimensional information with numerous variables. One of my advantages is that I have experience in both IS-behavior and IS-econ studies. My master’s thesis is a typical behavioral study using expectation-confirmation model to study online users’ continuous participant with structural equation model. My PhD career mainly focuses on IS-econ, but I am still interested in behavioral and psychological study and the third essay in my dissertation is a behavioral paper.


Manuscripts under Review


Work Papers


Work in Progress


References

 

Jan Stallaert

 

 

Jing Peng

 

 

Xinxin Li

  • Associate Professor of Operations and Information Management
  • School of Business, University of Connecticut